mlgb 0.0.7

MLGB is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch.

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MLGB means Machine Learning of the Great Boss, and is called 「妙计包」.
MLGB is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch.

• Advantages
• Supported Models
• Installation
• Getting Started
• Code Examples
• Citation

Advantages

• Easy! Use mlgb.get_model(model_name, **kwargs) to get a complex model.
• Fast! Better performance through better code.
• Enjoyable! 50+ ranking & matching models to use, 2 languages(TensorFlow & PyTorch) to deploy.

Supported Models

ID	Model Name	Paper Link	Paper Team	Paper Year
:open_file_folder: Ranking-Model::Normal :point_down:
1	LR	Predicting Clicks: Estimating the Click-Through Rate for New Ads	Microsoft	2007
2	PLM/MLR	Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction	Alibaba	2017
3	MLP/DNN	Neural Networks for Pattern Recognition	Christopher M. Bishop(Microsoft, 1997-Present), Foreword by Geoffrey Hinton.	1995
4	DLRM	Deep Learning Recommendation Model for Personalization and Recommendation Systems	Facebook(Meta)	2019
5	MaskNet	MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask	Weibo(Sina)	2021
6	DCM/DeepCross	Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features	Microsoft	2016
7	DCN	DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems, v1	Google(Alphabet)	2017, 2020
8	EDCN	Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models	Huawei	2021
9	FM	Factorization Machines	Steffen Rendle(Google, 2013-Present)	2010
10	FFM	Field-aware Factorization Machines for CTR Prediction	NTU	2016
11	HOFM	Higher-Order Factorization Machines	NTT	2016
12	FwFM	Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising	Junwei Pan(Yahoo), etc.	2018, 2020
13	FmFM	FM^2: Field-matrixed Factorization Machines for Recommender Systems	Yahoo	2021
14	FEFM	FIELD-EMBEDDED FACTORIZATION MACHINES FOR CLICK-THROUGH RATE PREDICTION	Harshit Pande(Adobe)	2020, 2021
15	AFM	Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks	ZJU&NUS(Jun Xiao(ZJU), Xiangnan He(NUS), etc.)	2017
16	LFM	Learning Feature Interactions with Lorentzian Factorization Machine	EBay	2019
17	IFM	An Input-aware Factorization Machine for Sparse Prediction	THU	2019
18	DIFM	A Dual Input-aware Factorization Machine for CTR Prediction	THU	2020
19	FNN	Deep Learning over Multi-field Categorical Data – A Case Study on User Response Prediction	UCL(Weinan Zhang(UCL, SJTU), etc.)	2016
20	PNN	Product-based Neural Networks for User Response	SJTU&UCL(Yanru Qu(SJTU), Weinan Zhang(SJTU, UCL), etc.)	2016
21	PIN	Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data	Huawei(Yanru Qu(Huawei(2017.3-2018.3), SJTU), Weinan Zhang(SJTU, UCL), etc.)	2018
22	ONN/NFFM	Operation-aware Neural Networks for User Response Prediction	NJU	2019
23	AFN	Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions	SJTU	2019, 2020
24	NFM	Neural Factorization Machines for Sparse Predictive Analytics	NUS(Xiangnan He(NUS))	2017
25	WDL	Wide & Deep Learning for Recommender Systems	Google(Alphabet)	2016
26	DeepFM	DeepFM: A Factorization-Machine based Neural Network for CTR Prediction	Huawei	2017
27	DeepFEFM	FIELD-EMBEDDED FACTORIZATION MACHINES FOR CLICK-THROUGH RATE PREDICTION	Harshit Pande(Adobe)	2020, 2021
28	FLEN	FLEN: Leveraging Field for Scalable CTR Prediction	Meitu	2019, 2020
29	CCPM	A Convolutional Click Prediction Model	CASIA	2015
30	FGCNN	Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction	Huawei	2019
31	XDeepFM	xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems	Microsoft(Jianxun Lian(USTC, Microsoft(2018.7-Present)), etc.)	2018
32	FiBiNet	FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction	Weibo(Sina)	2019
33	AutoInt	AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks	PKU	2018, 2019
:open_file_folder: Ranking-Model::Sequential :point_down:
34	GRU4Rec	Session-based Recommendations with Recurrent Neural Networks	Telefonica	2015, 2016
35	Caser	Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding	SFU	2018
36	SASRec	Self-Attentive Sequential Recommendation	UCSD	2018
37	BERT4Rec	BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer	Alibaba	2019
38	BST	Behavior Sequence Transformer for E-commerce Recommendation in Alibaba	Alibaba	2019
39	DIN	Deep Interest Network for Click-Through Rate Prediction, v1	Alibaba	2017, 2018
40	DIEN	Deep Interest Evolution Network for Click-Through Rate Prediction	Alibaba	2018
41	DSIN	Deep Session Interest Network for Click-Through Rate Prediction	Alibaba	2019
:open_file_folder: Ranking-Model::Multitask :point_down:
42	SharedBottom	An Overview of Multi-Task Learning in Deep Neural Networks	Sebastian Ruder(InsightCentre)	2017
43	ESMM	Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate	Alibaba	2018
44	MMoE	Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts	Google(Alphabet)	2018
45	PLE	Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations	Tencent	2020
46	PEPNet	PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information	Kuaishou	2023
:open_file_folder: Matching-Model :point_down:
47	NCF	Neural Collaborative Filtering	NUS(Xiangnan He(NUS), etc)	2017
48	MatchFM	Factorization Machines	Steffen Rendle(Google, 2013-Present)	2010
49	DSSM	Learning deep structured semantic models for web search using clickthrough data	Microsoft	2013
50	EBR	Embedding-based Retrieval in Facebook Search	Facebook(Meta)	2020
51	YoutubeDNN	Deep Neural Networks for YouTube Recommendations	Google(Alphabet)	2016
52	MIND	Multi-Interest Network with Dynamic Routing for Recommendation at Tmall	Alibaba	2019

Installation

# PYPI
pip install --upgrade mlgb

# Conda
conda install conda-forge::mlgb

Getting Started

import mlgb

# parameters of get_model:
help(mlgb.get_model)

"""
get_model(feature_names, model_name='LR', task='binary', aim='ranking', lang='TensorFlow', device=None, seed=None, **kwargs)
    :param feature_names: tuple(tuple(dict)), must. Embedding need vocabulary size and custom embed_dim of features.
    :param model_name: str, default 'LR'. Union[`mlgb.ranking_models`, `mlgb.matching_models`, `mlgb.mtl_models`]
    :param task: str, default 'binary'. Union['binary', 'regression', 'multiclass:{int}']
    :param aim: str, default 'ranking'. Union['ranking', 'matching', 'mtl']
    :param lang: str, default 'TensorFlow'. Union['TensorFlow', 'PyTorch', 'tf', 'torch']
    :param device: Optional[str, int], default None. Only for PyTorch.
    :param seed: Optional[int], default None.
    :param **kwargs: more model parameters by `mlgb.get_model_help(model_name)`.
"""

# parameters of model:
mlgb.get_model_help(model_name='LR', lang='tf')

"""
 class LR(tf.keras.src.models.model.Model)
 |  LR(feature_names, task='binary', seed=None, inputs_if_multivalued=False, inputs_if_sequential=False, inputs_if_embed_dense=False, embed_dim=32, embed_2d_dim=None, embed_l2=0.0, embed_initializer=None, pool_mv_mode='Pooling:average', pool_mv_axis=2, pool_mv_l2=0.0, pool_mv_initializer=None, pool_seq_mode='Pooling:average', pool_seq_axis=1, pool_seq_l2=0.0, pool_seq_initializer=None, linear_if_bias=True, linear_l1=0.0, linear_l2=0.0, linear_initializer=None)
 |  
 |  Methods defined here:
 |  
 |  __init__(self, feature_names, task='binary', seed=None, inputs_if_multivalued=False, inputs_if_sequential=False, inputs_if_embed_dense=False, embed_dim=32, embed_2d_dim=None, embed_l2=0.0, embed_initializer=None, pool_mv_mode='Pooling:average', pool_mv_axis=2, pool_mv_l2=0.0, pool_mv_initializer=None, pool_seq_mode='Pooling:average', pool_seq_axis=1, pool_seq_l2=0.0, pool_seq_initializer=None, linear_if_bias=True, linear_l1=0.0, linear_l2=0.0, linear_initializer=None)
 |      Model Name: LR(LinearOrLogisticRegression)
 |      Paper Team: Microsoft
 |      Paper Year: 2007
 |      Paper Name: <Predicting Clicks: Estimating the Click-Through Rate for New Ads>
 |      Paper Link: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/predictingclicks.pdf
 |      
 |      Task Inputs Parameters:
 |          :param feature_names: tuple(tuple(dict)), must. Embedding need vocabulary size and custom embed_dim of features.
 |          :param task: str, default 'binary'. Union['binary', 'regression']
 |          :param seed: Optional[int], default None.
 |          :param inputs_if_multivalued: bool, default False.
 |          :param inputs_if_sequential: bool, default False.
 |          :param inputs_if_embed_dense: bool, default False.
 |          :param embed_dim: int, default 32.
 |          :param embed_2d_dim: Optional[int], default None. When None, each field has own embed_dim by feature_names.
 |          :param embed_l2: float, default 0.0.
 |          :param embed_initializer: Optional[str], default None. When None, activation judge first, xavier_normal end.
 |          :param pool_mv_mode: str, default 'Pooling:average'. Pooling mode of multivalued inputs. Union[
 |                              'Attention', 'Weighted', 'Pooling:max', 'Pooling:average', 'Pooling:sum']
 |          :param pool_mv_axis: int, default 2. Pooling axis of multivalued inputs.
 |          :param pool_mv_l2: float, default 0.0. When pool_mv_mode is in ('Weighted', 'Attention'), it works.
 |          :param pool_mv_initializer: Optional[str], default None. When None, activation judge first,
 |                              xavier_normal end. When pool_mv_mode is in ('Weighted', 'Attention'), it works.
 |          :param pool_seq_mode: str, default 'Pooling:average'. Pooling mode of sequential inputs. Union[
 |                              'Attention', 'Weighted', 'Pooling:max', 'Pooling:average', 'Pooling:sum']
 |          :param pool_seq_axis: int, default 1. Pooling axis of sequential inputs.
 |          :param pool_seq_l2: float, default 0.0. When pool_seq_mode is in ('Weighted', 'Attention'), it works.
 |          :param pool_seq_initializer: Optional[str], default None. When None, activation judge first,
 |                              xavier_normal end. When pool_seq_mode is in ('Weighted', 'Attention'), it works.
 |      
 |      Task Model Parameters:
 |          :param linear_if_bias: bool, default True.
 |          :param linear_l1: float, default 0.0.
 |          :param linear_l2: float, default 0.0.
 |          :param linear_initializer: Optional[str], default None. When None, activation judge first, xavier_normal end.
"""

Code Examples

Code Examples
TensorFlow
PyTorch

Citation

If you use this for research, please cite it using the following BibTeX entry. Thanks.

@misc{uliontse2020mlgb,
  author = {UlionTse},
  title = {MLGB is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub Repository},
  howpublished = {\url{https://github.com/UlionTse/mlgb}},
}